code: def test_merge_method_seq_concat(self):
      bx1 = BLayer.Input(shape=(10,))
      bx2 = BLayer.Input(shape=(10,))
      by1 = BLayer.Dense(12, activation="sigmoid")(bx1)
      bbranch1_node = BModel(bx1, by1)(bx1)
      bbranch2 = BSequential()
      bbranch2.add(BLayer.Dense(12, input_dim=10))
      bbranch2_node = bbranch2(bx2)
      bz = BLayer.merge([bbranch1_node, bbranch2_node], mode="concat")
      bmodel = BModel([bx1, bx2], bz)
  
      kx1 = KLayer.Input(shape=(10,))
      kx2 = KLayer.Input(shape=(10,))
      ky1 = KLayer.Dense(12, activation="sigmoid")(kx1)
      kbranch1_node = KModel(kx1, ky1)(kx1)
      kbranch2 = KSequential()
      kbranch2.add(KLayer.Dense(12, input_dim=10))
      kbranch2_node = kbranch2(kx2)
      kz = KLayer.merge([kbranch1_node, kbranch2_node], mode="concat")
      kmodel = KModel([kx1, kx2], kz)
  
      input_data = [
          np.random.random([2, 10]),
          np.random.random([2, 10]),
      ]
      self.compare_newapi(
          kmodel, bmodel, input_data, self.convert_two_dense_model
      )
  
imports: import pytest
  from test.bigdl.test_utils import BigDLTestCase
  import bigdl.dllib.nn.keras.layers.layer
  import keras.layers
  import keras.backend
  from bigdl.dllib.keras.converter import WeightsConverter
  from bigdl.dllib.feature.dataset.dataset import *
  from bigdl.dllib.nn.keras.layers.topology import Model
  from bigdl.dllib.nn.keras.layers.topology import Sequential
  from keras.engine import merge, Model
  from keras.models import Sequential
label_desc: Use image node_id 17 for calling the TestKerasAPI obj's underlying member method code with example usage: obj.test_merge_method_seq_concat() without return types